First, we load, filter, and merge the data sets.
How does the data set looks like
Applied tresholds are indicated by grey horizontal line.
#Apply tresholds
data <- subset(data, Mean_Puncta_mito_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mito_Number_Object_Number < 1200)
data <- subset(data, mito_MeanArea < 0.04)
data <- subset(data, mito_MeanCount < 0.2)
data <- subset(data, mito_MeanLength < 0.1)
data <- subset(data, Branchpoints < 200)
#Save data set
write.csv(data, file = "results/tables/data_mito.csv")
Cell counts per cell line:
#data <- read.csv("results/tables/data_mito.csv")
table(data$Metadata_SampleID)
##
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015
## 110 118 125 118 101 114
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002
## 87 105 89 167 131 76
Mean cell count:
mean(table(data$Metadata_SampleID))
## [1] 111.75
Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.
Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## Mean_Puncta_mito_AreaShape_Area ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 13185.5 13206.3 -6588.7 13177.5 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2591 -0.7390 -0.1397 0.5596 3.6687
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 51.01 7.142
## Residual 1066.69 32.660
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 73.945 3.475 21.279
## Disease_statesPD 6.561 4.563 1.438
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mito_AreaShape_Area
## Chisq Df Pr(>Chisq)
## Disease_state 2.0668 1 0.1505
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mito_Number_Object_Number ~ Disease_state + (1 |
## Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 19004.5 19025.3 -9498.2 18996.5 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8091 -0.8211 -0.1309 0.7812 2.5681
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 3299 57.44
## Residual 81869 286.13
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 565.65 28.35 19.955
## Disease_statesPD -10.11 37.24 -0.272
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mito_Number_Object_Number
## Chisq Df Pr(>Chisq)
## Disease_state 0.0738 1 0.7859
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mito_Intensity_MeanIntensity_Corr_mito ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -4093.7 -4072.9 2050.8 -4101.7 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1579 -0.7031 -0.1618 0.6287 4.3208
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.0007024 0.02650
## Residual 0.0026666 0.05164
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.14448 0.01205 11.991
## Disease_statesPD -0.01822 0.01579 -1.154
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mito_Intensity_MeanIntensity_Corr_mito
## Chisq Df Pr(>Chisq)
## Disease_state 1.3317 1 0.2485
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: mito_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -9414.2 -9393.4 4711.1 -9422.2 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6079 -0.6999 -0.2744 0.4379 3.8921
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.68e-06 0.001296
## Residual 5.13e-05 0.007163
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0102080 0.0006527 15.639
## Disease_statesPD 0.0010092 0.0008580 1.176
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mito_MeanArea
## Chisq Df Pr(>Chisq)
## Disease_state 1.3836 1 0.2395
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: mito_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -4425.0 -4404.2 2216.5 -4433.0 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5945 -0.8088 -0.2109 0.6250 2.9401
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 7.809e-05 0.008837
## Residual 2.116e-03 0.046002
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.075443 0.004397 17.158
## Disease_statesPD -0.002436 0.005778 -0.422
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mito_MeanCount
## Chisq Df Pr(>Chisq)
## Disease_state 0.1777 1 0.6733
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ObjectSkeleton_NumberBranchEnds_mito_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 10055.6 10076.4 -5023.8 10047.6 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6671 -0.7047 -0.1464 0.5690 4.9810
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.512 1.23
## Residual 104.186 10.21
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 16.4336 0.6966 23.592
## Disease_statesPD -0.3257 0.9177 -0.355
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.759
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_NumberBranchEnds_mito_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.1259 1 0.7227
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 13178.2 13199.0 -6585.1 13170.2 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6695 -0.7268 -0.1804 0.4718 4.2733
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 27.24 5.219
## Residual 1065.67 32.645
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 45.253 2.705 16.728
## Disease_statesPD 1.675 3.558 0.471
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Branchpoints
## Chisq Df Pr(>Chisq)
## Disease_state 0.2217 1 0.6377
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## ObjectSkeleton_TotalObjectSkeletonLength_mito_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 18029.6 18050.4 -9010.8 18021.6 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5013 -0.7576 -0.1928 0.5112 4.5958
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 552 23.49
## Residual 39843 199.61
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 273.809 13.427 20.393
## Disease_statesPD -6.965 17.691 -0.394
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.759
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mito_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.155 1 0.6938
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mito_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -6350.7 -6329.9 3179.4 -6358.7 1337
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7079 -0.7971 -0.1527 0.6045 2.9565
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.187e-05 0.003445
## Residual 5.049e-04 0.022471
## Number of obs: 1341, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.033490 0.001805 18.550
## Disease_statesPD -0.001349 0.002375 -0.568
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.760
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mito_MeanLength
## Chisq Df Pr(>Chisq)
## Disease_state 0.3224 1 0.5702